1
|
Yeo TS, Kim JS, Kim HJ, Chung CK. Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction. Neuroimage 2024; 297:120727. [PMID: 39069222 DOI: 10.1016/j.neuroimage.2024.120727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/12/2024] [Accepted: 07/08/2024] [Indexed: 07/30/2024] Open
Abstract
This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.
Collapse
Affiliation(s)
- Tae Soo Yeo
- Dept. of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea; Clinical Research Institute, Konkuk University Medical Center, Seoul, Republic of Korea
| | - June Sic Kim
- Clinical Research Institute, Konkuk University Medical Center, Seoul, Republic of Korea; Research Institute of Biomedical Science and Technology, Konkuk University, Seoul, Republic of Korea.
| | - Hong June Kim
- Clinical Research Institute, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Chun Kee Chung
- Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea
| |
Collapse
|
2
|
Affiliation(s)
- Laureen D Hachem
- Division of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Gustavo Balbinot
- Division of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Michael G Fehlings
- Division of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
3
|
Ramirez-Nava AG, Mercado-Gutierrez JA, Quinzaños-Fresnedo J, Toledo-Peral C, Vega-Martinez G, Gutierrez MI, Pacheco-Gallegos MDR, Hernández-Arenas C, Gutiérrez-Martínez J. Functional electrical stimulation therapy controlled by a P300-based brain-computer interface, as a therapeutic alternative for upper limb motor function recovery in chronic post-stroke patients. A non-randomized pilot study. Front Neurol 2023; 14:1221160. [PMID: 37669261 PMCID: PMC10470638 DOI: 10.3389/fneur.2023.1221160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/03/2023] [Indexed: 09/07/2023] Open
Abstract
Introduction Up to 80% of post-stroke patients present upper-limb motor impairment (ULMI), causing functional limitations in daily activities and loss of independence. UMLI is seldom fully recovered after stroke when using conventional therapeutic approaches. Functional Electrical Stimulation Therapy (FEST) controlled by Brain-Computer Interface (BCI) is an alternative that may induce neuroplastic changes, even in chronic post-stroke patients. The purpose of this work was to evaluate the effects of a P300-based BCI-controlled FEST intervention, for ULMI recovery of chronic post-stroke patients. Methods A non-randomized pilot study was conducted, including 14 patients divided into 2 groups: BCI-FEST, and Conventional Therapy. Assessments of Upper limb functionality with Action Research Arm Test (ARAT), performance impairment with Fugl-Meyer assessment (FMA), Functional Independence Measure (FIM) and spasticity through Modified Ashworth Scale (MAS) were performed at baseline and after carrying out 20 therapy sessions, and the obtained scores compared using Chi square and Mann-Whitney U statistical tests (𝛼 = 0.05). Results After training, we found statistically significant differences between groups for FMA (p = 0.012), ARAT (p < 0.001), and FIM (p = 0.025) scales. Discussion It has been shown that FEST controlled by a P300-based BCI, may be more effective than conventional therapy to improve ULMI after stroke, regardless of chronicity. Conclusion The results of the proposed BCI-FEST intervention are promising, even for the most chronic post-stroke patients often relegated from novel interventions, whose expected recovery with conventional therapy is very low. It is necessary to carry out a randomized controlled trial in the future with a larger sample of patients.
Collapse
Affiliation(s)
- Ana G. Ramirez-Nava
- Neurological Rehabilitation Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | - Jorge A. Mercado-Gutierrez
- Medical Engineering Research Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | - Jimena Quinzaños-Fresnedo
- Neurological Rehabilitation Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | - Cinthya Toledo-Peral
- Medical Engineering Research Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | - Gabriel Vega-Martinez
- Medical Engineering Research Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | - Mario Ibrahin Gutierrez
- Consejo Nacional de Humanidades, Ciencias y Tecnologías - Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | | | - Claudia Hernández-Arenas
- Neurological Rehabilitation Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| | - Josefina Gutiérrez-Martínez
- Medical Engineering Research Division, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Tlalpan, Mexico
| |
Collapse
|
4
|
Morone G, Pirrera A, Iannone A, Giansanti D. Development and Use of Assistive Technologies in Spinal Cord Injury: A Narrative Review of Reviews on the Evolution, Opportunities, and Bottlenecks of Their Integration in the Health Domain. Healthcare (Basel) 2023; 11:healthcare11111646. [PMID: 37297786 DOI: 10.3390/healthcare11111646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/15/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Assistive technologies are increasingly taking a leading role in supporting people with spinal cord injury (SCI). This narrative review of reviews intends to contribute by making a map point investigating the integration of ATs in SCI. The methodology of the review was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment using specific parameters. The outcome highlighted the following: -The evolution of ATs considered in the context of SCI, considering ATs as both products and/or services in standalone and/or networked devices, and as processes of delivery. -Innovative technologies could play an important role in improving the quality of life and in minimizing costs in healthcare. -The international scientific community has identified ATs as one of the six strategic development areas in SCI. The overview also allowed the detection of some problems: (I) The ethical and regulatory aspects have been addressed in a weak way and only in specific and limited cases. (II) There is a lack of studies on the use and applications of ATs in SCI with a focus in multiple domains (e.g., costs, acceptance, dissemination, problems, regulatory aspects, ethical aspects, and other issues important for integration into the health domain). This review highlights the need for further studies and activities focused on integrating consensus in multiple domains, including ethics and regulations, to aid researchers and decision-makers in the field.
Collapse
Affiliation(s)
- Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- San Raffaele Institute of Sulmona, 67039 Sulmona, Italy
| | | | - Antonio Iannone
- Association "Relazionarti", 05100 Terni, Italy
- CREA Italian National Research Body, 00184 Roma, Italy
| | | |
Collapse
|
5
|
Ferrero L, Quiles V, Ortiz M, Iáñez E, Gil-Agudo Á, Azorín JM. Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. iScience 2023; 26:106675. [PMID: 37250318 PMCID: PMC10214472 DOI: 10.1016/j.isci.2023.106675] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 03/06/2023] [Accepted: 04/11/2023] [Indexed: 05/31/2023] Open
Abstract
This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two patients with spinal cord injuries. Five able-bodied subjects underwent a virtual reality (VR) training session to accelerate training with the BCI. Results from this group were compared with a control group of five able-bodied subjects, and it was found that the employment of shorter training by VR did not reduce the effectiveness of the BCI and even improved it in some cases. Patients gave positive feedback about the system and were able to handle experimental sessions without reaching high levels of physical and mental exertion. These results are promising for the inclusion of BCI in rehabilitation programs, and future research should investigate the potential of the MI-based BCI system.
Collapse
Affiliation(s)
- Laura Ferrero
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- The European University of Brain and Technology (NeurotechEU)
| | - Vicente Quiles
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
| | - Mario Ortiz
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- The European University of Brain and Technology (NeurotechEU)
| | - Eduardo Iáñez
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
| | | | - José M. Azorín
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI), Valencia, Spain
- The European University of Brain and Technology (NeurotechEU)
| |
Collapse
|
6
|
Higa S, Yamada K, Kamisato S. Intelligent Eye-Controlled Electric Wheelchair Based on Estimating Visual Intentions Using One-Dimensional Convolutional Neural Network and Long Short-Term Memory. SENSORS (BASEL, SWITZERLAND) 2023; 23:4028. [PMID: 37112369 PMCID: PMC10145036 DOI: 10.3390/s23084028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 06/19/2023]
Abstract
When an electric wheelchair is operated using gaze motion, eye movements such as checking the environment and observing objects are also incorrectly recognized as input operations. This phenomenon is called the "Midas touch problem", and classifying visual intentions is extremely important. In this paper, we develop a deep learning model that estimates the user's visual intention in real time and an electric wheelchair control system that combines intention estimation and the gaze dwell time method. The proposed model consists of a 1DCNN-LSTM that estimates visual intention from feature vectors of 10 variables, such as eye movement, head movement, and distance to the fixation point. The evaluation experiments classifying four types of visual intentions show that the proposed model has the highest accuracy compared to other models. In addition, the results of the driving experiments of the electric wheelchair implementing the proposed model show that the user's efforts to operate the wheelchair are reduced and that the operability of the wheelchair is improved compared to the traditional method. From these results, we concluded that visual intentions could be more accurately estimated by learning time series patterns from eye and head movement data.
Collapse
Affiliation(s)
- Sho Higa
- Graduate School of Engineering and Science, University of the Ryukyus, Nishihara 903-0213, Japan
| | - Koji Yamada
- Department of Engineering, University of the Ryukyus, Nishihara 903-0213, Japan;
| | - Shihoko Kamisato
- Department of Information and Communication Systems Engineering, National Institute of Technology, Okinawa College, Nago 905-2171, Japan;
| |
Collapse
|
7
|
Girão AF, Serrano MC, Completo A, Marques PAAP. Is Graphene Shortening the Path toward Spinal Cord Regeneration? ACS NANO 2022; 16:13430-13467. [PMID: 36000717 PMCID: PMC9776589 DOI: 10.1021/acsnano.2c04756] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Along with the development of the next generation of biomedical platforms, the inclusion of graphene-based materials (GBMs) into therapeutics for spinal cord injury (SCI) has potential to nourish topmost neuroprotective and neuroregenerative strategies for enhancing neural structural and physiological recovery. In the context of SCI, contemplated as one of the most convoluted challenges of modern medicine, this review first provides an overview of its characteristics and pathophysiological features. Then, the most relevant ongoing clinical trials targeting SCI, including pharmaceutical, robotics/neuromodulation, and scaffolding approaches, are introduced and discussed in sequence with the most important insights brought by GBMs into each particular topic. The current role of these nanomaterials on restoring the spinal cord microenvironment after injury is critically contextualized, while proposing future concepts and desirable outputs for graphene-based technologies aiming to reach clinical significance for SCI.
Collapse
Affiliation(s)
- André F. Girão
- Centre
for Mechanical Technology and Automation (TEMA), Department of Mechanical
Engineering, University of Aveiro (UA), Aveiro, 3810-193, Portugal
- Instituto
de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la
Cruz 3, Madrid, 28049, Spain
- (A.F.G.)
| | - María Concepcion Serrano
- Instituto
de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la
Cruz 3, Madrid, 28049, Spain
- (M.C.S.)
| | - António Completo
- Centre
for Mechanical Technology and Automation (TEMA), Department of Mechanical
Engineering, University of Aveiro (UA), Aveiro, 3810-193, Portugal
| | - Paula A. A. P. Marques
- Centre
for Mechanical Technology and Automation (TEMA), Department of Mechanical
Engineering, University of Aveiro (UA), Aveiro, 3810-193, Portugal
- (P.A.A.P.M.)
| |
Collapse
|
8
|
Lazarou I, Nikolopoulos S, Georgiadis K, Oikonomou VP, Mariakaki A, Kompatsiaris I. Exploring the Connection of Brain Computer Interfaces and Multimedia Use With the Social Integration of People With Various Motor Disabilities: A Questionnaire-Based Usability Study. Front Digit Health 2022; 4:846963. [PMID: 35990018 PMCID: PMC9385967 DOI: 10.3389/fdgth.2022.846963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Abstract
We have designed a platform to aid people with motor disabilities to be part of digital environments, in order to create digitally and socially inclusive activities that promote their quality of life. To evaluate in depth the impact of the platform on social inclusion indicators across patients with various motor disabilities, we constructed a questionnaire in which the following indicators were assessed: (i) Well Being, (ii) Empowerment, (iii) Participation, (iv) Social Capital, (v) Education, and (vi) Employment. In total 30 participants (10 with Neuromuscular Disorders-NMD, 10 with Spinal Cord Injury-SCI, and 10 with Parkinson's Disease-PD) used the platform for ~1 month, and its impact on social inclusion indicators was measured before and after the usage. Moreover, monitoring mechanisms were used to track computer usage as well as an online social activity. Finally, testimonials and experimenter input were collected to enrich the study with qualitative understanding. All participants were favorable to use the suggested platform, while they would prefer it for longer periods of time in order to become “re-awakened” to possibilities of expanded connection and inclusion, while it became clear that the platform has to offer them further the option to use it in a reclining position. The present study has clearly shown that the challenge of social inclusion cannot be tackled solely with technology and it needs to integrate persuasive design elements that foster experimentation and discovery.
Collapse
Affiliation(s)
- Ioulietta Lazarou
- Centre for Research and Technology Hellas (CERTH-ITI), Information Technologies Institute, Thessaloniki, Greece
- *Correspondence: Ioulietta Lazarou
| | - Spiros Nikolopoulos
- Centre for Research and Technology Hellas (CERTH-ITI), Information Technologies Institute, Thessaloniki, Greece
| | - Kostas Georgiadis
- Centre for Research and Technology Hellas (CERTH-ITI), Information Technologies Institute, Thessaloniki, Greece
| | - Vangelis P. Oikonomou
- Centre for Research and Technology Hellas (CERTH-ITI), Information Technologies Institute, Thessaloniki, Greece
| | - Agnes Mariakaki
- Muscular Dystrophy Association-Hellas (MDA-Hellas), Hellas, Athens, Greece
| | - Ioannis Kompatsiaris
- Centre for Research and Technology Hellas (CERTH-ITI), Information Technologies Institute, Thessaloniki, Greece
| |
Collapse
|
9
|
Pitt KM, McKelvey M, Weissling K. The perspectives of augmentative and alternative communication experts on the clinical integration of non-invasive brain-computer interfaces. BRAIN-COMPUTER INTERFACES 2022. [DOI: 10.1080/2326263x.2022.2057758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Kevin M. Pitt
- Department of Special Education and Communication Disorders, University of Nebraska–Lincoln, Lincoln, NE, USA
| | - Miechelle McKelvey
- Department of Communication Disorders, University of Nebraska Kearney Kearney, NE, USA
| | - Kristy Weissling
- Department of Special Education and Communication Disorders, University of Nebraska–Lincoln, Lincoln, NE, USA
| |
Collapse
|
10
|
Gutierrez-Martinez J, Mercado-Gutierrez JA, Carvajal-Gámez BE, Rosas-Trigueros JL, Contreras-Martinez AE. Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions. Front Hum Neurosci 2021; 15:772837. [PMID: 34899220 PMCID: PMC8656949 DOI: 10.3389/fnhum.2021.772837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.
Collapse
Affiliation(s)
- Josefina Gutierrez-Martinez
- División de Investigación en Ingeniería Médica, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City, Mexico
| | - Jorge A. Mercado-Gutierrez
- División de Investigación en Ingeniería Médica, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City, Mexico
| | | | | | | |
Collapse
|
11
|
Corticospinal Motor Circuit Plasticity After Spinal Cord Injury: Harnessing Neuroplasticity to Improve Functional Outcomes. Mol Neurobiol 2021; 58:5494-5516. [PMID: 34341881 DOI: 10.1007/s12035-021-02484-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
Spinal cord injury (SCI) is a devastating condition that affects approximately 294,000 people in the USA and several millions worldwide. The corticospinal motor circuitry plays a major role in controlling skilled movements and in planning and coordinating movements in mammals and can be damaged by SCI. While axonal regeneration of injured fibers over long distances is scarce in the adult CNS, substantial spontaneous neural reorganization and plasticity in the spared corticospinal motor circuitry has been shown in experimental SCI models, associated with functional recovery. Beneficially harnessing this neuroplasticity of the corticospinal motor circuitry represents a highly promising therapeutic approach for improving locomotor outcomes after SCI. Several different strategies have been used to date for this purpose including neuromodulation (spinal cord/brain stimulation strategies and brain-machine interfaces), rehabilitative training (targeting activity-dependent plasticity), stem cells and biological scaffolds, neuroregenerative/neuroprotective pharmacotherapies, and light-based therapies like photodynamic therapy (PDT) and photobiomodulation (PMBT). This review provides an overview of the spontaneous reorganization and neuroplasticity in the corticospinal motor circuitry after SCI and summarizes the various therapeutic approaches used to beneficially harness this neuroplasticity for functional recovery after SCI in preclinical animal model and clinical human patients' studies.
Collapse
|
12
|
Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges. SENSORS 2021; 21:s21062084. [PMID: 33809721 PMCID: PMC8002299 DOI: 10.3390/s21062084] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/05/2021] [Accepted: 03/11/2021] [Indexed: 11/17/2022]
Abstract
Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.
Collapse
|
13
|
Zulauf-Czaja A, Al-Taleb MKH, Purcell M, Petric-Gray N, Cloughley J, Vuckovic A. On the way home: a BCI-FES hand therapy self-managed by sub-acute SCI participants and their caregivers: a usability study. J Neuroeng Rehabil 2021; 18:44. [PMID: 33632262 PMCID: PMC7905902 DOI: 10.1186/s12984-021-00838-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Regaining hand function is the top priority for people with tetraplegia, however access to specialised therapy outwith clinics is limited. Here we present a system for hand therapy based on brain-computer interface (BCI) which uses a consumer grade electroencephalography (EEG) device combined with functional electrical stimulation (FES), and evaluate its usability among occupational therapists (OTs) and people with spinal cord injury (SCI) and their family members. METHODS Users: Eight people with sub-acute SCI (6 M, 2F, age 55.4 ± 15.6) and their caregivers (3 M, 5F, age 45.3 ± 14.3); four OTs (4F, age 42.3 ± 9.8). User Activity: Researchers trained OTs; OTs subsequently taught caregivers to set up the system for the people with SCI to perform hand therapy. Hand therapy consisted of attempted movement (AM) of one hand to lower the power of EEG sensory-motor rhythm in the 8-12 Hz band and thereby activate FES which induced wrist flexion and extension. Technology: Consumer grade wearable EEG, multichannel FES, custom made BCI application. LOCATION Research space within hospital. Evaluation: donning times, BCI accuracy, BCI and FES parameter repeatability, questionnaires, focus groups and interviews. RESULTS Effectiveness: The BCI accuracy was 70-90%. Efficiency: Median donning times decreased from 40.5 min for initial session to 27 min during last training session (N = 7), dropping to 14 min on the last self-managed session (N = 3). BCI and FES parameters were stable from session to session. Satisfaction: Mean satisfaction with the system among SCI users and caregivers was 3.68 ± 0.81 (max 5) as measured by QUEST questionnaire. Main facilitators for implementing BCI-FES technology were "seeing hand moving", "doing something useful for the loved ones", good level of computer literacy (people with SCI and caregivers), "active engagement in therapy" (OT), while main barriers were technical complexity of setup (all groups) and "lack of clinical evidence" (OT). CONCLUSION BCI-FES has potential to be used as at home hand therapy by people with SCI or stroke, provided it is easy to use and support is provided. Transfer of knowledge of operating BCI is possible from researchers to therapists to users and caregivers. Trial registration Registered with NHS GG&C on December 6th 2017; clinicaltrials.gov reference number NCT03257982, url: https://clinicaltrials.gov/ct2/show/NCT03257982 .
Collapse
Affiliation(s)
- Anna Zulauf-Czaja
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK.
| | - Manaf K H Al-Taleb
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK.,Wasit University, Wasit, Iraq
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Elizabeth University Hospital, Glasgow, Queen, UK
| | - Nina Petric-Gray
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK
| | - Jennifer Cloughley
- Queen Elizabeth National Spinal Injuries Unit, Elizabeth University Hospital, Glasgow, Queen, UK
| | - Aleksandra Vuckovic
- Biomedical Engineering Research Division, University of Glasgow, Glasgow, UK
| |
Collapse
|
14
|
Draaisma LR, Wessel MJ, Hummel FC. Neurotechnologies as tools for cognitive rehabilitation in stroke patients. Expert Rev Neurother 2020; 20:1249-1261. [DOI: 10.1080/14737175.2020.1820324] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Laurijn R. Draaisma
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
| | - Maximilian J. Wessel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
| | - Friedhelm C. Hummel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
- Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland
| |
Collapse
|
15
|
Shamsi F, Haddad A, Zadeh LN. Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2869-2872. [PMID: 33018605 DOI: 10.1109/embc44109.2020.9175627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86-80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating pain/no-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.
Collapse
|
16
|
Belkacem AN, Jamil N, Palmer JA, Ouhbi S, Chen C. Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients. Front Neurosci 2020; 14:692. [PMID: 32694979 PMCID: PMC7339951 DOI: 10.3389/fnins.2020.00692] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 06/08/2020] [Indexed: 02/01/2023] Open
Abstract
All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.
Collapse
Affiliation(s)
- Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Jason A. Palmer
- Department of Neurological Diagnosis and Restoration, Osaka University, Suita, Japan
| | - Sofia Ouhbi
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
| |
Collapse
|
17
|
Melo MC, Macedo DR, Soares AB. Divergent Findings in Brain Reorganization After Spinal Cord Injury: A Review. J Neuroimaging 2020; 30:410-427. [PMID: 32418286 DOI: 10.1111/jon.12711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/02/2020] [Accepted: 03/24/2020] [Indexed: 12/12/2022] Open
Abstract
Spinal cord injury (SCI) leads to a general lack of sensory and motor functions below the level of injury and may promote deafferentation-induced brain reorganization. Functional magnetic resonance imaging (fMRI) has been established as an essential tool in neuroscience research and can precisely map the spatiotemporal distribution of brain activity. Task-based fMRI experiments associated with the tongue, upper limbs, or lower limbs have been used as the primary paradigms to study brain reorganization following SCI. A review of the current literature on the subject shows one common trait: while most articles agree that brain networks are usually preserved after SCI, and that is not the case as some articles describe possible alterations in brain activation after the lesion. There is no consensus if those alterations indeed occur. In articles that show alterations, there is no agreement if they are transient or permanent. Besides, there is no consensus on which areas are most prone to activation changes, or on the intensity and direction (increase vs. decrease) of those possible changes. In this article, we present a critical review of the literature and trace possible reasons for those contradictory findings on brain reorganization following SCI. fMRI studies based on the ankle dorsiflexion, upper-limb, and tongue paradigms are used as case studies for the analyses.
Collapse
Affiliation(s)
- Mariana Cardoso Melo
- Biomedical Engineering Lab, Federal University of Uberlandia, Uberlandia, Minas Gerais, Brazil
| | - Dhainner Rocha Macedo
- Biomedical Engineering Lab, Federal University of Uberlandia, Uberlandia, Minas Gerais, Brazil
| | - Alcimar Barbosa Soares
- Biomedical Engineering Lab, Federal University of Uberlandia, Uberlandia, Minas Gerais, Brazil
| |
Collapse
|
18
|
Foldes ST, Boninger ML, Weber DJ, Collinger JL. Effects of MEG-based neurofeedback for hand rehabilitation after tetraplegia: preliminary findings in cortical modulations and grip strength. J Neural Eng 2020; 17:026019. [PMID: 32135525 DOI: 10.1088/1741-2552/ab7cfb] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Neurofeedback (NF) trains people to volitionally modulate their cortical activity to affect a behavioral outcome. We evaluated the feasibility of using NF to improve hand function after chronic cervical-level spinal cord injury (SCI) using biologically-relevant visual feedback of motor-related brain activity and an intuitive control scheme. APPROACH The NF system acquired magnetoencephalography (MEG) data in real-time to provide feedback of event-related desynchronization (ERD) measured over the sensorimotor cortex during attempted hand grasping. During brain control, stronger ERD resulting from attempted grasping drove the virtual hand towards a more closed grasp, while less ERD drove the hand more open. MAIN RESULTS Eight individuals with partial or complete hand impairment due to chronic SCI controlled the NF to perform a grasping task that increased in difficulty as the participants achieved success. During their first NF session, participants achieved an average success rate of 63.7 ± 6.4% (chance level of 13.9%). After as few as one intervention session, four of the seven individuals evaluated for ERD changes had significantly strengthened ERD and three of the four participants with measurable grip strength prior to NF had increased grip strength. Interestingly, both individuals who participated in a longer-term study (i.e. >8 NF sessions) had improved grip strength and significantly strengthened ERD. SIGNIFICANCE This study demonstrates that MEG-based NF training can change brain activity in individuals with hand impairment due to SCI and has the potential to induce acute changes in grip strength. Future studies will evaluate whether neuroplasticity induced with long term NF can improve hand function for those with moderate impairment.
Collapse
Affiliation(s)
- Stephen T Foldes
- VA Pittsburgh Healthcare System, Pittsburgh, PA, United States of America. Rehab Neural Engineering Labs, Departments of Physical Medicine and Rehabilitation and Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America. Center for the Neural Basis of Cognition, Pittsburgh, PA, United States of America. Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States of America
| | | | | | | |
Collapse
|
19
|
Letourneau S, Zewdie ET, Jadavji Z, Andersen J, Burkholder LM, Kirton A. Clinician awareness of brain computer interfaces: a Canadian national survey. J Neuroeng Rehabil 2020; 17:2. [PMID: 31907010 PMCID: PMC6945584 DOI: 10.1186/s12984-019-0624-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022] Open
Abstract
Background Individuals with severe neurological disabilities but preserved cognition, including children, are often precluded from connecting with their environments. Brain computer interfaces (BCI) are a potential solution where advancing technologies create new clinical opportunities. We evaluated clinician awareness as a modifiable barrier to progress and identified eligible populations. Methods We executed a national, population-based, cross-sectional survey of physician specialists caring for persons with severe disability. An evidence- and experience-based survey had three themes: clinician BCI knowledge, eligible populations, and potential impact. A BCI knowledge index was created and scored. Canadian adult and pediatric neurologists, physiatrists and a subset of developmental pediatricians were contacted. Secure, web-based software administered the survey via email with online data collection. Results Of 922 valid emails (664 neurologists, 253 physiatrists), 137 (15%) responded. One third estimated that ≥10% of their patients had severe neurological disability with cognitive capacity. BCI knowledge scores were low with > 40% identifying as less than “vaguely aware” and only 15% as “somewhat familiar” or better. Knowledge did not differ across specialties. Only 6 physicians (4%) had patients using BCI. Communication and wheelchair control rated highest for potentially improving quality of life. Most (81%) felt BCI had high potential to improve quality of life. Estimates suggested that > 13,000 Canadians (36 M population) might benefit from BCI technologies. Conclusions Despite high potential and thousands of patients who might benefit, BCI awareness among clinicians caring for disabled persons is poor. Further, functional priorities for BCI applications may differ between medical professionals and potential BCI users, perhaps reflecting that clinicians possess a less accurate understanding of the desires and needs of potential end-users. Improving knowledge and engaging both clinicians and patients could facilitate BCI program development to improve patient outcomes.
Collapse
Affiliation(s)
- Sasha Letourneau
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada
| | - Ephrem Takele Zewdie
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada
| | - Zeanna Jadavji
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada.,Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W, Calgary, AB, AB T2N 1N4, Canada
| | - John Andersen
- Department of Pediatrics, University of Alberta, 116 St. and 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Lee M Burkholder
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada.,Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W, Calgary, AB, AB T2N 1N4, Canada
| | - Adam Kirton
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada. .,Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 2500 University Drive N.W, Calgary, AB, AB T2N 1N4, Canada. .,Department of Pediatrics, University of Alberta, 116 St. and 85 Ave, Edmonton, AB T6G 2R3, Canada. .,Alberta Children's Hospital Research Institute, 28 Oki Drive S.W, Calgary, AB, T3B6A8, Canada. .,Hotchkiss Brain Institute, University of Calgary, 2500 University Drive N.W, Calgary, AB, T2N 1N4, Canada.
| |
Collapse
|
20
|
Klein E. Ethics and the emergence of brain-computer interface medicine. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:329-339. [PMID: 32164863 DOI: 10.1016/b978-0-444-63934-9.00024-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Brain-computer interface (BCI) technology will usher in profound changes to the practice of medicine. BCI devices, broadly defined as those capable of reading brain activity and translating this into operation of a device, will offer patients and clinicians new ways to address impairments of communication, movement, sensation, and mental health. These new capabilities will bring new responsibilities and raise a diverse set of ethical challenges. One way to understand and begin to address these challenges is to view them in terms of the goals of medicine. In this chapter, different ways in which BCI technology may subserve the goals of medicine is explored. This is followed by articulation of additional goals particularly relevant to BCI technology: neural diversity, neural privacy, agency, and authenticity. The goals of medicine provide a useful ethical framework for the introduction of BCI devices into medicine.
Collapse
Affiliation(s)
- Eran Klein
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States; Department of Philosophy, University of Washington, Seattle, WA, United States.
| |
Collapse
|
21
|
Abstract
A spinal cord injury (SCI) may result in impairments of motor, sensory, and autonomous functions below the injury level. Worldwide, the prevalence of SCI is 1:1000 and the incidence is between 4 and 9 new cases per 100,000 people per year. Most common causes for traumatic SCI are traffic accidents, falls, and violence. Nowadays, the proportion of patients with tetraplegia and paraplegia is equal. In industrialized countries, the percentage of nontraumatic injuries increases together with age. Most patients with initially preserved motor functions below the injury level show a substantial functional recovery, while three quarters of patients with initially complete SCI remain that way. In SCI, brain-computer interfaces (BCIs) may be used in the subacute phase as part of a restorative therapy program and, later, for control of assistive devices most needed by individuals with high cervical lesions. Research on structural and functional reorganization of the deefferented and deafferented brain after SCI is inconclusive mainly because of varying methods of analysis and the heterogeneity of the investigated populations. A better characterization of study participants with SCI together with documentation of confounding factors such as antispasticity medication or neuropathic pain is indicated.
Collapse
Affiliation(s)
- Rüdiger Rupp
- Experimental Neurorehabilitation, Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany.
| |
Collapse
|
22
|
Arnin J, Kahani D, Lakany H, Conway BA. Evaluation of Different Signal Processing Methods in Time and Frequency Domain for Brain-Computer Interface Applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:235-238. [PMID: 30440381 DOI: 10.1109/embc.2018.8512193] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one's movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such real-time BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.
Collapse
|
23
|
Gottlieb A, Plotnik M, Kizony R, Katsarou Z, Bostantjopoulou S, Zeilig G. Identification of clinically related requirements of a novel assistive device for people with a high spinal cord injury. PLoS One 2019; 14:e0218393. [PMID: 31251758 PMCID: PMC6599142 DOI: 10.1371/journal.pone.0218393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 05/31/2019] [Indexed: 11/18/2022] Open
Abstract
People with spinal cord injuries (SCI), and particularly with high level lesions, can potentially lose the ability to effectively operate computers. The Multimedia Authoring and Management using your Eyes and Mind (MAMEM) project aims to design and produce a novel assistive device to support computer use by individuals with SCI and other disabilities. The solution harnesses eye tracking and brain waves, as measured by encephalography (EEG), to manipulate common computer functions. This paper describes the first step in the project, during which we defined clinically related requirements of the assistive device. These definitions were based on data from three sources: (1) a narrative review; (2) a focus group of SCI rehabilitation professionals; and (3) structured questionnaires administrated to potential computer users with SCI, addressing computer-use habits, barriers, and needs. We describe both the collection of data from each source and the clinically related requirements extracted. The novel three-source requirement assessment method is discussed, and the advantages and disadvantages of each data source are reported. In conclusion, we suggest that this approach makes it possible to organize, discuss, and prioritize the requirements, and to create a work program while planning the device. This increases our level of certainty that the efficacy and adequacy of the assistive device will be maximized, in terms of the clinical needs of users.
Collapse
Affiliation(s)
- Amihai Gottlieb
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Neurological Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- * E-mail:
| | - Racheli Kizony
- Department of Occupational Therapy, University of Haifa, Haifa, Israel
| | - Zoe Katsarou
- Neurology, Hippokration Hospital, Thessaloniki, Greece
| | | | - Gabi Zeilig
- Department of Neurological Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Physical Medicine & Rehabilitation, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
24
|
Wang Z, Zhou Y, Chen L, Gu B, Yi W, Liu S, Xu M, Qi H, He F, Ming D. BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training. IEEE Trans Neural Syst Rehabil Eng 2019; 27:780-787. [PMID: 30843846 DOI: 10.1109/tnsre.2019.2903685] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Motor imagery-based brain-computer interface (MI-BCI) controlling functional electrical stimulation (FES) is promising for disabled patients to restore their motor functions. However, it remains unclear how much the BCI part can contribute to the functional coupling between the brain and muscle. Specifically, whether it can enhance the cerebral activation for motor training? Here, we investigate the electroencephalographic and cerebral hemodynamic responses for MI-BCI-FES training and MI-FES training, respectively. Twelve healthy subjects were recruited in the motor training study when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. Compared with the MI-FES training conditions, the MI-BCI-FES could induce significantly stronger event-related desynchronization (ERD) and blood oxygen response, which demonstrates that BCI indeed plays a functional role in the closed-loop motor training. Therefore, this paper verifies the feasibility of using BCI to train motor functions in a closed-loop manner.
Collapse
|
25
|
Keyl P, Schneiders M, Schuld C, Franz S, Hommelsen M, Weidner N, Rupp R. Differences in Characteristics of Error-Related Potentials Between Individuals With Spinal Cord Injury and Age- and Sex-Matched Able-Bodied Controls. Front Neurol 2019; 9:1192. [PMID: 30766510 PMCID: PMC6365444 DOI: 10.3389/fneur.2018.01192] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 12/27/2018] [Indexed: 12/25/2022] Open
Abstract
Background: Non-invasive brain-computer interfaces (BCI) represent an emerging technology for enabling persons with impaired or lost grasping and reaching functions due to high spinal cord injury (SCI) to control assistive devices. A major drawback of BCIs is a high rate of false classifications. The robustness and performance of BCIs might be improved using cerebral electrophysiological correlates of error recognition (error-related potentials, ErrPs). As ErrPs have never been systematically examined in subjects with SCI, this study compares the characteristics of ErrPs in individuals with SCI with those of able-bodied control subjects. Methods: ErrPs at FCz and Cz were analyzed in 11 subjects with SCI (9 male, median age 28 y) and in 11 sex- and age-matched controls. Moving a shoulder joystick according to a visual cue, subjects received feedback about the match/mismatch of the performed movement. ErrPs occurring after "error"-feedback were evaluated by comparing means of voltage values within three consecutive time windows after feedback (wP1, wN1, wP2 containing peak voltages P1, N1, P2) using repeated-measurement analysis of variance. Results: In the control group, mean voltage values for the "error" and "correct" feedback condition differed significantly around N1 (FCz: 254 ms, Cz: 252 ms) and P2 (FCz: 347 ms, Cz: 345 ms), but not around P1 (FCz: 181 ms, Cz: 179 ms). ErrPs of the control and the SCI group showed similar morphology, however mean amplitudes of ErrPs were significantly smaller in individuals with SCI compared to controls for wN1 (FCz: control = -1.55 μV, SCI = -0.27 μV, p = 0.02; Cz: control = -1.03 μV, SCI = 0.11 μV, p = 0.04) and wP2 (FCz: control = 2.79 μV, SCI = 1.29 μV, p = 0.011; Cz: control = 2.12 μV, SCI = 0.81 μV, p = 0.003). Mean voltage values in wP1, wN1, and wP2 did not correlate significantly with either chronicity after or level of injury. Conclusion: The morphology of ErrPs in subjects with and without SCI is comparable, however, with reduced mean amplitude in wN1 and wP2 in the SCI group. Further studies should evaluate whether ErrP-classification can be used for online correction of false BCI-commands in individuals with SCI.
Collapse
Affiliation(s)
- Philipp Keyl
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Matthias Schneiders
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Schuld
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Franz
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Nobert Weidner
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Rüdiger Rupp
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| |
Collapse
|
26
|
Yousefi R, Sereshkeh AR, Chau T. Exploiting error-related potentials in cognitive task based BCI. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aaee99] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
27
|
Höller Y, Thomschewski A, Uhl A, Bathke AC, Nardone R, Leis S, Trinka E, Höller P. HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury. Front Neurol 2018; 9:955. [PMID: 30510537 PMCID: PMC6252382 DOI: 10.3389/fneur.2018.00955] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 10/24/2018] [Indexed: 12/16/2022] Open
Abstract
Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.
Collapse
Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Andreas Uhl
- Department of Computer Sciences, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Arne C Bathke
- Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Raffaele Nardone
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Department of Neurology, Franz Tappeiner Hospital, Merano, Italy
| | - Stefan Leis
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| |
Collapse
|
28
|
Wang Z, Chen L, Yi W, Gu B, Liu S, An X, Xu M, Qi H, He F, Wan B, Ming D. Enhancement of cortical activation for motor imagery during BCI-FES training .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:2527-2530. [PMID: 30440922 DOI: 10.1109/embc.2018.8512749] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Brain-computer Interfaces (BCIs) provide a direct pathway between the brain and the outward environment. Specifically, motor imagery (MI)-based BCI controlling functional electric stimulation (FES) is a promising approach for disabled patients with intact mind to restore or rehabilitate their motor functions. This study probed for the improvement of cortical activation for motor imagery during the closed-loop BCI-FES training. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to inspect the cortical activation for four different training strategies, i.e. MI-BCI-FES, MI-FES, MI and FES. Compared with the other three training conditions, the MI-BCI-FES could achieve stronger cortical activation viewing from the event-related desynchronization (ERD) and the blood oxygen response. The results demonstrate that the closed-loop MI training using BCI-FES can prospectively increase the cortical activation of motor cortical areas.
Collapse
|
29
|
López-Larraz E, Sarasola-Sanz A, Irastorza-Landa N, Birbaumer N, Ramos-Murguialday A. Brain-machine interfaces for rehabilitation in stroke: A review. NeuroRehabilitation 2018; 43:77-97. [PMID: 30056435 DOI: 10.3233/nre-172394] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.
Collapse
Affiliation(s)
- E López-Larraz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - A Sarasola-Sanz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School (IMPRS) for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany.,Neurotechnology, Tecnalia Research & Innovation, San Sebastián, Spain
| | - N Irastorza-Landa
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School (IMPRS) for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany.,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - N Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,Wyss Center for Bio and Neuro Engineering, Geneva, Switzerland
| | - A Ramos-Murguialday
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,Neurotechnology, Tecnalia Research & Innovation, San Sebastián, Spain
| |
Collapse
|
30
|
López-Larraz E, Ibáñez J, Trincado-Alonso F, Monge-Pereira E, Pons JL, Montesano L. Comparing Recalibration Strategies for Electroencephalography-Based Decoders of Movement Intention in Neurological Patients with Motor Disability. Int J Neural Syst 2018; 28:1750060. [DOI: 10.1142/s0129065717500605] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention.
Collapse
Affiliation(s)
- Eduardo López-Larraz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany
- Instituto de Investigación de Ingeniería de Aragón (I3A), Departamento de Informática e Ingeniería de Sistemas, University of Zaragoza, María de Luna 1, 50015, Zaragoza, Spain
| | - Jaime Ibáñez
- Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, 3rd Floor, Clinical Neurosciences Building, 33, Queen Square, London, WC1N 3BG, UK
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Av Doctor Arce, 37, 28002 Madrid, Spain
| | - Fernando Trincado-Alonso
- Biomechanics and Technical Aids Unit, Hospital Nacional de Parapléjicos, Finca La Peraleda s/n, 45071 Toledo, Spain
| | - Esther Monge-Pereira
- Departamento de Fisioterapia, Terapia Ocupacional, Rehabilitación y Medicina Física, Facultad de CC de la Salud, Universidad Rey Juan Carlos, Av. de Atenas, s/n, 28922, Alcorcón, Spain
| | - José Luis Pons
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Av Doctor Arce, 37, 28002 Madrid, Spain
- Tecnológico de Monterrey, Mexico
| | - Luis Montesano
- Instituto de Investigación de Ingeniería de Aragón (I3A), Departamento de Informática e Ingeniería de Sistemas, University of Zaragoza, María de Luna 1, 50015, Zaragoza, Spain
- Bit&Brain Technologies SL, Paseo de Sagasta, 19, 50008, Zaragoza, Spain
| |
Collapse
|
31
|
Dong Y, Raif KE, Determan SC, Gai Y. Decoding spatial attention with EEG and virtual acoustic space. Physiol Rep 2018; 5:5/22/e13512. [PMID: 29180483 PMCID: PMC5704085 DOI: 10.14814/phy2.13512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 10/30/2017] [Indexed: 11/24/2022] Open
Abstract
Decoding spatial attention based on brain signals has wide applications in brain–computer interface (BCI). Previous BCI systems mostly relied on visual patterns or auditory stimulation (e.g., loudspeakers) to evoke synchronous brain signals. There would be difficulties to cover a large range of spatial locations with such a stimulation protocol. The present study explored the possibility of using virtual acoustic space and a visual‐auditory matching paradigm to overcome this issue. The technique has the flexibility of generating sound stimulation from virtually any spatial location. Brain signals of eight human subjects were obtained with a 32‐channel Electroencephalogram (EEG). Two amplitude‐modulated noise or speech sentences carrying distinct spatial information were presented concurrently. Each sound source was tagged with a unique modulation phase so that the phase of the recorded EEG signals indicated the sound being attended to. The phase‐tagged sound was further filtered with head‐related transfer functions to create the sense of virtual space. Subjects were required to pay attention to the sound source that best matched the location of a visual target. For all the subjects, the phase of a single sound could be accurately reflected over the majority of electrodes based on EEG responses of 90 s or less. The electrodes providing significant decoding performance on auditory attention were fewer and may require longer EEG responses. The reliability and efficiency of decoding with a single electrode varied with subjects. Overall, the virtual acoustic space protocol has the potential of being used in practical BCI systems.
Collapse
Affiliation(s)
- Yue Dong
- Biomedical Engineering Department, Parks College of Engineering, Aviation and Technology Saint Louis University, St Louis, Missouri
| | - Kaan E Raif
- Biomedical Engineering Department, Parks College of Engineering, Aviation and Technology Saint Louis University, St Louis, Missouri
| | - Sarah C Determan
- Biomedical Engineering Department, Parks College of Engineering, Aviation and Technology Saint Louis University, St Louis, Missouri
| | - Yan Gai
- Biomedical Engineering Department, Parks College of Engineering, Aviation and Technology Saint Louis University, St Louis, Missouri
| |
Collapse
|
32
|
Meena YK, Cecotti H, Wong-Lin K, Prasad G. A multimodal interface to resolve the Midas-Touch problem in gaze controlled wheelchair. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:905-908. [PMID: 29060019 DOI: 10.1109/embc.2017.8036971] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Human-computer interaction (HCI) research has been playing an essential role in the field of rehabilitation. The usability of the gaze controlled powered wheelchair is limited due to Midas-Touch problem. In this work, we propose a multimodal graphical user interface (GUI) to control a powered wheelchair that aims to help upper-limb mobility impaired people in daily living activities. The GUI was designed to include a portable and low-cost eye-tracker and a soft-switch wherein the wheelchair can be controlled in three different ways: 1) with a touchpad 2) with an eye-tracker only, and 3) eye-tracker with soft-switch. The interface includes nine different commands (eight directions and stop) and integrated within a powered wheelchair system. We evaluated the performance of the multimodal interface in terms of lap-completion time, the number of commands, and the information transfer rate (ITR) with eight healthy participants. The analysis of the results showed that the eye-tracker with soft-switch provides superior performance with an ITR of 37.77 bits/min among the three different conditions (p<;0.05). Thus, the proposed system provides an effective and economical solution to the Midas-Touch problem and extended usability for the large population of disabled users.
Collapse
|
33
|
Schalk G, Allison BZ. Noninvasive Brain–Computer Interfaces. Neuromodulation 2018. [DOI: 10.1016/b978-0-12-805353-9.00026-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
34
|
Thomschewski A, Höller Y, Höller P, Leis S, Trinka E. High Amplitude EEG Motor Potential during Repetitive Foot Movement: Possible Use and Challenges for Futuristic BCIs That Restore Mobility after Spinal Cord Injury. Front Neurosci 2017; 11:362. [PMID: 28690497 PMCID: PMC5481367 DOI: 10.3389/fnins.2017.00362] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 06/09/2017] [Indexed: 11/23/2022] Open
Abstract
Recent advances in neuroprostheses provide us with promising ideas of how to improve the quality of life in people suffering from impaired motor functioning of upper and lower limbs. Especially for patients after spinal cord injury (SCI), futuristic devices that are controlled by thought via brain-computer interfaces (BCIs) might be of tremendous help in managing daily tasks and restoring at least some mobility. However, there are certain problems arising when trying to implement BCI technology especially in such a heterogenous patient group. A plethora of processes occurring after the injuries change the brain's structure as well as its functionality collectively referred to as neuroplasticity. These changes are very different between individuals, leading to an increasing interest to reveal the exact changes occurring after SCI. In this study we investigated event-related potentials (ERPs) derived from electroencephalography (EEG) signals recorded during the (attempted) execution and imagination of hand and foot movements in healthy subjects and patients with SCI. As ERPs and especially early components are of interest for BCI research we aimed to investigate differences between 22 healthy volunteers and 7 patients (mean age = 51.86, SD = 15.49) suffering from traumatic or non-traumatic SCI since 2–314 months (mean = 116,57, SD = 125,55). We aimed to explore differences in ERP responses as well as the general presence of component that might be of interest to further consider for incorporation into BCI research. In order to match the real-life situation of BCIs for controlling neuroprostheses, we worked on small trial numbers (<25), only. We obtained a focal potential over Pz in ten healthy participants but in none of the patients after lenient artifact rejection. The potential was characterized by a high amplitude, it correlated with the repeated movements (6 times in 6 s) and in nine subjects it significantly differed from a resting condition. Furthermore, there are strong arguments against possible confounding factors leading to the potential's appearance. This phenomenon, occurring when movements are repeatedly conducted, might represent a possible potential to be used in futuristic BCIs and further studies should try to investigate the replicability of its appearance.
Collapse
Affiliation(s)
- Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical UniversitySalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center SalzburgSalzburg, Austria.,Department of Psychology, Paris-Lodron University of SalzburgSalzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical UniversitySalzburg, Austria.,Department of Psychology, Paris-Lodron University of SalzburgSalzburg, Austria.,Center for Cognitive Neuroscience SalzburgSalzburg, Austria
| | - Peter Höller
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical UniversitySalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center SalzburgSalzburg, Austria
| | - Stefan Leis
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical UniversitySalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center SalzburgSalzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical UniversitySalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center SalzburgSalzburg, Austria.,Center for Cognitive Neuroscience SalzburgSalzburg, Austria
| |
Collapse
|
35
|
Osuagwu BCA, Wallace L, Fraser M, Vuckovic A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study. J Neural Eng 2016; 13:065002. [PMID: 27739405 DOI: 10.1088/1741-2560/13/6/065002] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To compare neurological and functional outcomes between two groups of hospitalised patients with subacute tetraplegia. APPROACH Seven patients received 20 sessions of brain computer interface (BCI) controlled functional electrical stimulation (FES) while five patients received the same number of sessions of passive FES for both hands. The neurological assessment measures were event related desynchronization (ERD) during movement attempt, Somatosensory evoked potential (SSEP) of the ulnar and median nerve; assessment of hand function involved the range of motion (ROM) of wrist and manual muscle test. MAIN RESULTS Patients in both groups initially had intense ERD during movement attempt that was not restricted to the sensory-motor cortex. Following the treatment, ERD cortical activity restored towards the activity in able-bodied people in BCI-FES group only, remaining wide-spread in FES group. Likewise, SSEP returned in 3 patients in BCI-FES group, having no changes in FES group. The ROM of the wrist improved in both groups. Muscle strength significantly improved for both hands in BCI-FES group. For FES group, a significant improvement was noticed for right hand flexor muscles only. SIGNIFICANCE Combined BCI-FES therapy results in better neurological recovery and better improvement of muscle strength than FES alone. For spinal cord injured patients, BCI-FES should be considered as a therapeutic tool rather than solely a long-term assistive device for the restoration of a lost function.
Collapse
Affiliation(s)
- Bethel C A Osuagwu
- School of Engineering, College of Engineering and Science, James Watt Building (south)University of Glasgow, Glasgow, G12 8QQ UK. Stoke Mandeville Spinal Research, National Spinal Injuries Centre, Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK
| | | | | | | |
Collapse
|
36
|
López-Larraz E, Trincado-Alonso F, Rajasekaran V, Pérez-Nombela S, Del-Ama AJ, Aranda J, Minguez J, Gil-Agudo A, Montesano L. Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation. Front Neurosci 2016; 10:359. [PMID: 27536214 PMCID: PMC4971110 DOI: 10.3389/fnins.2016.00359] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 07/19/2016] [Indexed: 12/11/2022] Open
Abstract
The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).
Collapse
Affiliation(s)
- Eduardo López-Larraz
- Departamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain
| | | | - Vijaykumar Rajasekaran
- Institute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain
| | - Soraya Pérez-Nombela
- Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
| | - Antonio J Del-Ama
- Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
| | - Joan Aranda
- Institute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain
| | - Javier Minguez
- Departamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain; Bit & Brain TechnologiesZaragoza, Spain
| | - Angel Gil-Agudo
- Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
| | - Luis Montesano
- Departamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain
| |
Collapse
|
37
|
Tidoni E, Gergondet P, Fusco G, Kheddar A, Aglioti SM. The Role of Audio-Visual Feedback in a Thought-Based Control of a Humanoid Robot: A BCI Study in Healthy and Spinal Cord Injured People. IEEE Trans Neural Syst Rehabil Eng 2016; 25:772-781. [PMID: 28113631 DOI: 10.1109/tnsre.2016.2597863] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and-place task by means of a visual evoked potentials BCI system. Participants observed the remote environment from the robot's perspective through a head mounted display. Human-footsteps and computer-beep sounds were used as synchronous/asynchronous auditory feedback. Healthy participants achieved better placing accuracy when listening to human footstep sounds relative to a computer-generated sound. SCI people demonstrated more difficulty in steering the robot during asynchronous auditory feedback conditions. Importantly, subjective reports highlighted that the BCI mask overlaying the display did not limit the observation of the scenario and the feeling of being in control of the robot. Overall, the data seem to suggest that sensorimotor-related information may improve the control of external devices. Further studies are required to understand how the contribution of residual sensory channels could improve the reliability of BCI systems.
Collapse
|
38
|
Zeyl T, Yin E, Keightley M, Chau T. Adding Real-Time Bayesian Ranks to Error-Related Potential Scores Improves Error Detection and Auto-Correction in a P300 Speller. IEEE Trans Neural Syst Rehabil Eng 2016; 24:46-56. [DOI: 10.1109/tnsre.2015.2461495] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
39
|
Pazzaglia M, Molinari M. The embodiment of assistive devices-from wheelchair to exoskeleton. Phys Life Rev 2015; 16:163-75. [PMID: 26708357 DOI: 10.1016/j.plrev.2015.11.006] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Accepted: 11/24/2015] [Indexed: 01/18/2023]
Abstract
Spinal cord injuries (SCIs) place a heavy burden on the healthcare system and have a high personal impact and marked socio-economic consequences. Clinically, no absolute cure for these conditions exists. However, in recent years, there has been an increased focus on new robotic technologies that can change the frame we think about the prognosis for recovery and for treating some functions of the body affected after SCIs. This review has two goals. The first is to assess the possibility of the embodiment of functional assistive tools after traumatic disruption of the neural pathways between the brain and the body. To this end, we will examine how altered sensorimotor information modulates the sense of the body in SCI. The second goal is to map the phenomenological experience of using external tools that typically extend the potential of the body physically impaired by SCI. More specifically, we will focus on the difference between the perception of one's physically augmented and non-augmented affected body based on observable and measurable behaviors. We discuss potential clinical benefits of enhanced embodiment of the external objects by way of multisensory interventions. This review argues that the future evolution of human robotic technologies will require adopting an embodied approach, taking advantage of brain plasticity to allow bionic limbs to be mapped within the neural circuits of physically impaired individuals.
Collapse
Affiliation(s)
- Mariella Pazzaglia
- Department of Psychology, University of Rome 'La Sapienza', Via dei Marsi 78, 00185 Rome, Italy; IRCCS Fondazione Santa Lucia, Via Ardeatina 306, 00179 Rome, Italy.
| | - Marco Molinari
- IRCCS Fondazione Santa Lucia, Via Ardeatina 306, 00179 Rome, Italy
| |
Collapse
|
40
|
Gerber S, Horenko I. Improving clustering by imposing network information. SCIENCE ADVANCES 2015; 1:e1500163. [PMID: 26601225 PMCID: PMC4643807 DOI: 10.1126/sciadv.1500163] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Accepted: 06/25/2015] [Indexed: 06/05/2023]
Abstract
Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface.
Collapse
Affiliation(s)
- Susanne Gerber
- Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| | - Illia Horenko
- Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| |
Collapse
|
41
|
Tidoni E, Tieri G, Aglioti SM. Re-establishing the disrupted sensorimotor loop in deafferented and deefferented people: The case of spinal cord injuries. Neuropsychologia 2015; 79:301-9. [PMID: 26115603 DOI: 10.1016/j.neuropsychologia.2015.06.029] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 06/15/2015] [Accepted: 06/21/2015] [Indexed: 11/26/2022]
Abstract
Acting efficiently in the world depends on the activity of motor and somatosensory systems, the integration of which is necessary for the proper functioning of the sensorimotor loop (SL). Profound alterations of SL functioning follow spinal cord injury (SCI), a condition that brings about a disconnection of the body from the brain. Such disconnection creates a substantial deprivation of somatosensorial inputs and motor outputs. Consequent somatic deficits and motor paralysis affect the body below the lesion level. A complete restoration of normal functions of the SL cannot be expected until basic neuroscience has found a way to re-establish the interrupted neural connectivity. Meanwhile, studies should focus on the development of technical solutions for dealing with the disruption of the sensorimotor loop. This review discusses the structural and functional adaptive reorganization of the brain after SCI, and the maladaptive mechanisms that impact on the processing of body related information, which alter motor imagery strategies and EEG signals. Studies that show how residual functions (e.g. face tactile sensitivity) may help people to restore a normal body image are also reviewed. Finally, data on how brain and residual body signals may be used to improve brain computer interface systems is discussed in relation to the issue of how such systems may help SCI people to re-enter the world and interact with objects and other individuals.
Collapse
Affiliation(s)
- E Tidoni
- Department of Psychology, University of Rome "La Sapienza", Rome, Italy; Fondazione Santa Lucia, IRCCS, Rome, Italy.
| | - G Tieri
- Fondazione Santa Lucia, IRCCS, Rome, Italy; Braintrends Ltd, Applied Neuroscience, Rome, Italy
| | - S M Aglioti
- Department of Psychology, University of Rome "La Sapienza", Rome, Italy; Fondazione Santa Lucia, IRCCS, Rome, Italy.
| |
Collapse
|
42
|
Serruya MD. Bottlenecks to clinical translation of direct brain-computer interfaces. Front Syst Neurosci 2014; 8:226. [PMID: 25520632 PMCID: PMC4251316 DOI: 10.3389/fnsys.2014.00226] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 11/10/2014] [Indexed: 12/17/2022] Open
Abstract
Despite several decades of research into novel brain-implantable devices to treat a range of diseases, only two—cochlear implants for sensorineural hearing loss and deep brain stimulation for movement disorders—have yielded any appreciable clinical benefit. Obstacles to translation include technical factors (e.g., signal loss due to gliosis or micromotion), lack of awareness of current clinical options for patients that the new therapy must outperform, traversing between federal and corporate funding needed to support clinical trials, and insufficient management expertise. This commentary reviews these obstacles preventing the translation of promising new neurotechnologies into clinical application and suggests some principles that interdisciplinary teams in academia and industry could adopt to enhance their chances of success.
Collapse
Affiliation(s)
- Mijail D Serruya
- Department of Neurology, Thomas Jefferson University Philadelphia, PA, USA
| |
Collapse
|
43
|
Vuckovic A, Pineda JA, LaMarca K, Gupta D, Guger C. Interaction of BCI with the underlying neurological conditions in patients: pros and cons. FRONTIERS IN NEUROENGINEERING 2014; 7:42. [PMID: 25477814 PMCID: PMC4235364 DOI: 10.3389/fneng.2014.00042] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 11/03/2014] [Indexed: 11/13/2022]
Affiliation(s)
| | - Jaime A Pineda
- Cognitive Science Department, University of California San Diego, La Jolla, CA, USA
| | - Kristen LaMarca
- Clinical Psyhology, California School of Professional Psychology San Diego, CA, USA
| | - Disha Gupta
- Burke Rehabilitation Center, Burke-Cornell Medical Research Institute White Plains, NY, USA
| | - Christoph Guger
- Guger Technologies OG, g.tec medical engineering GmbH Graz, Austria
| |
Collapse
|